Computerized system and method for obtaining information about a region of an object

ABSTRACT

A method, system and computer readable medium for providing information about a region of a sample. The method includes (i) obtaining, by an imager, multiple images of the region; wherein the multiple images differ from each other by at least one parameter (ii) receiving or generating multiple reference images; (iii) generating multiple difference images that represent differences between the multiple images and the multiple reference images; (iv) calculating a set of region pixel attributes, (v) calculating a set of noise attributes, based on multiple sets of region pixels attributes of the multiple region pixels; and (vi) determining for each region pixel, whether the region pixel represents a defect based on a relationship between the set of noise attributes and the set of region pixel attributes of the pixel.

BACKGROUND OF THE INVENTION

As the design rule shrinks, wafer inspection tools are required todetect ever smaller defects. Previously, defect detection was limitedmainly by laser power and detectors noise. In present day (and probablyin future) tools, detection are frequently limited by light scattered bythe roughness of the patterns printed on wafer and in particular by lineedge roughness. These irregularities scatter light with random phases,which can combine to generate bright spots on the detector (speckles),almost undistinguishable from a real defect. Current filteringtechniques are not effective in removing these speckles, as their shapesin the standard bright field or dark field channels are very similar tothose of real defects.

There is a growing need to provide a reliable defect detection system.

SUMMARY

There may be provided a method for obtaining information about a regionof a sample, the method may include (a) obtaining, by an imager,multiple images of the region; wherein the multiple images differ fromeach other by at least one parameter selected out of illuminationspectrum, collection spectrum, illumination polarization, collectionpolarization, angle of illumination, angle of collection, and sensingtype; wherein the obtaining of the multiple images comprisesilluminating the region and collecting radiation from the region;wherein the region comprises multiple region pixels; (b) receiving orgenerating multiple reference images; (c) generating, by an imageprocessor, multiple difference images that represent differences betweenthe multiple images and the multiple reference images; (d) calculating aset of region pixel attributes for each region pixel of the multipleregion pixels; wherein the calculating is based on pixels of themultiple difference images; (e) calculating a set of noise attributes,based on multiple sets of region pixels attributes of the multipleregion pixels; and (f) determining for each region pixel, whether theregion pixel represents a defect based on a relationship between the setof noise attributes and the set of region pixel attributes of the pixel.

There may be provided a computerized system for obtaining informationabout a region of a sample, the system may include an imager thatcomprises optics and an image processor; wherein the imager isconfigured to obtain multiple images of the region; wherein the multipleimages differ from each other by at least one parameter selected out ofillumination spectrum, collection spectrum, illumination polarization,collection polarization, angle of illumination, and angle of collection;wherein the obtaining of the multiple images comprises illuminating theregion and collecting radiation from the region; wherein the regioncomprises multiple region pixels; wherein the computerized system isconfigured to receive or generate multiple reference images; wherein theimage processor is configured to: generate multiple difference imagesthat represent differences between the multiple images and the multiplereference images; calculate a set of region pixel attributes for eachregion pixel of the multiple region pixels; wherein the set of regionpixel attributes are calculated based on pixels of the multipledifference images; calculate a set of noise attributes, based onmultiple sets of region pixels attributes of the multiple region pixels;and determine, for each region pixel, whether the region pixelrepresents a defect based on a relationship between the set of noiseattributes and the set of region pixel attributes of the pixel.

There may be provided a non-transitory computer-readable medium that maystore instructions that cause a computerized system to obtain, by animager of the computerized system, multiple images of a region of anobject; wherein the multiple images differ from each other by at leastone parameter selected out of illumination spectrum, collectionspectrum, illumination polarization, collection polarization, angle ofillumination, angle of collection, and sensing type; wherein theobtaining of the multiple images comprises illuminating the region andcollecting radiation from the region; wherein the region comprisesmultiple region pixels; receive or generate multiple reference images;generate, by an image processor of the computerized system, multipledifference images that represent differences between the multiple imagesand the multiple reference images; calculate a set of region pixelattributes for each region pixel of the multiple region pixels; whereinthe calculating is based on pixels of the multiple difference images;calculate a set of noise attributes, based on multiple sets of regionpixels attributes of the multiple region pixels; and determine, for eachregion pixel, whether the region pixel represents a defect based on arelationship between the set of noise attributes and the set of regionpixel attributes of the pixel.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 illustrates an example of a method;

FIG. 2 illustrates an example of a system and a sample;

FIG. 3 illustrates an example of a scanning of a region of an object;

FIG. 4 illustrates an example of a system and a sample;

FIG. 5 illustrates an example of a system and a sample;

FIG. 6 illustrates an example of various images;

FIG. 7 illustrates an example of various images; and

FIG. 8 illustrates an example of various images.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

Any reference in the specification to a method should be applied mutatismutandis to a system capable of executing the method and should beapplied mutatis mutandis to a non-transitory computer-readable mediumthat stores instructions that once executed by a computer result in theexecution of the method.

Any reference in the specification to a system should be applied mutatismutandis to a method that can be executed by the system and should beapplied mutatis mutandis to a non-transitory computer-readable mediumthat stores instructions that can be executed by the system.

Any reference in the specification to a non-transitory computer-readablemedium should be applied mutatis mutandis to a system capable ofexecuting the instructions stored in the non-transitorycomputer-readable medium and should be applied mutatis mutandis tomethod that can be executed by a computer that reads the instructionsstored in the non-transitory computer-readable medium.

There may be provided a system, method and a computer-readable mediumfor using multiple detection channels in combination with ahigh-dimensional analysis of data acquired from the multiple detectionchannels. The suggested method takes into account much more informationfrom the interaction of the electromagnetic field and the wafer, with asignificant benefit to detection. An improvement of at least seventypercent was acquired using the listed below method.

FIG. 1 illustrates an example of method 300.

Method 300 may include steps 310, 320, 330, 340, 350 and 360.

Method 300 may start by step 310 of obtaining, by an imager, multipleimages of the region. The region may include multiple region pixels.

An imager is a module or unit that is configured to acquire the multipleimages.

The imager may be configured to illuminate the region with radiation,collect radiation from the region and detect the collected radiationfrom the sample. The imager may include optics, an image processor andmay include multiple detectors.

Step 310 may include illuminating a region of a sample, collectingradiation from the region and detecting the collected radiation from thesample.

The radiation may be ultraviolet (UV) radiation, deep UV radiation,extreme UV radiation or any other type of radiation.

It is assumed that a single radiation beam may scan the region—but itshould be noted that multiple radiation beams may scan multiple regionssimultaneously.

The multiple images differ from each other by at least one parameterselected out of (i) illumination spectrum (which is the spectralresponse of an illumination portion of the imager), collection spectrum(which is the spectral response of a collection portion of the imager),illumination polarization (which is the polarization imposed by theillumination portion of the imager), collection polarization (which isthe polarization imposed by the collection portion of the imager), angleof illumination (angle of illumination of the region by the illuminationportion of the imager), angle of collection, and a detection type (forexample—detecting amplitude and/or detecting phase).

The imager may include multiple detectors for generating the multipleimages. Different detectors may be allocated to detect radiation fromdifferent pupil segments of the multiple pupil segments—one detector perpupil segment. Each one of the multiple detectors may be located in aplane that is conjugate to the pupil plane. See, for example, detectors70 that are located in a plane that is conjugate to pupil plane 26 ofsystem 10 of FIG. 1.

The different pupil segments may not overlap, may completely non-overlapor only partially overlap. The pupil segments can be of equal shape andsize but at least two pupil segments may differ from each other by shapeand additionally or alternatively by size and/or a position on the exitpupil plane.

There may be more than four pupil segments.

The imager may include multiple detectors for generating the multipleimages. Different detectors may be allocated to detect radiations ofdifferent combinations of (a) polarization and (b) different pupilsegments of the multiple pupil segments. See, for example, detectors 70are allocated to detect radiation of a first polarization from differentpupil segments and detectors 70′ are allocated to detect radiation of asecond polarization from different pupil segments.

It should be noted that the pupil may not be segmented and each of themultiple detectors may be allocated to the entire pupil.

Step 310 may include obtaining the multiple images at the same point intime.

Alternatively, step 310 may include obtaining two or more of themultiple images at different points in time.

Step 320 may include receiving or generating multiple reference images.The reference images may be images obtained by imager of an area of thesample that differs from the region of the sample. The area and theregion may not overlap (for example when performing die to diecomparison), or may partially overlap (for example when performing cellto cell comparison). The reference images may be calculated in variousmanners—for example by processing computer-aided design (CAD)information of the region, by generating a golden reference, and thelike.

It should be noted that one image of the multiple images may be used asa reference image of another image of the multiple images.

Using a reference image that is not another image of the multiple imagesmay be beneficial—as it may provide more information about the region.

Step 330 may include generating, by the image processor, multipledifference images that represent differences between the multiple imagesand the multiple reference images.

Step 340 may include calculating a set of region pixel attributes foreach region pixel of the multiple region pixels. The calculating may bebased on pixels of the multiple difference images.

The set of pixel attributes of a region pixel may include data regardingthe region pixel and neighbouring region pixels of the region pixel.

Step 350 may include calculating a set of noise attributes, based onmultiple sets of region pixels attributes of the multiple region pixels.

Step 350 may include calculating a covariance matrix.

Step 350 may include:

-   -   a. Calculating for each region pixel, a set of covariance values        that represent the covariance between different attributes of        the set of region pixel attributes of the region pixel.    -   b. Calculating the covariance matrix based on multiple sets of        covariance values of the multiple region pixels.

Step 360 may include determining for each region pixel, whether theregion pixel represents a defect based on a relationship between the setof noise attributes and the set of region pixel attributes of the pixel.

Step 360 may also be responsive to a set of attributes of an actualdefect, or to a set of attributes of an estimated defect. The estimateddefect may be a model of the defect.

Step 360 may include:

-   -   a. Comparing, to a threshold, a product of a multiplication        involving (i) a set of attributes of the region pixel, (ii) the        covariance matrix, and (iii) a set of attributes of an actual        noise or of an estimated noise.

FIG. 2 illustrates an example of system 10 and sample 100.

FIG. 2 illustrates an allocation of nine detectors to nine pupilsegments—one detector per pupil segment.

FIG. 2 also illustrates the pupil plane 26.

System 10 include radiation source 30, optics such as first beamsplitter 40 and objective lens 50, detectors 70 and image processor 90.

The optics may include any combination of optical elements that maydetermine one or more optical properties (such as shape, size,polarization) of the radiation beam from radiation source 30, maydetermine the path of the radiation beam from radiation source 30, maydetermine one or more optical properties (such as shape, size,polarization) of one or more radiation beams scattered and/or reflectedby the sample and determine the path of the one or more radiationbeams—and direct the one or more radiation beams towards the detectors70.

The optics may include lenses, grids, telescopes, beam splitters,polarizers, reflectors, deflectors, apertures, and the like.

In FIG. 2 the radiation beam from radiation source 30 passes throughfirst beam splitter 40 and is focused by objective lens 50 onto a regionof sample 22. Radiation beam from the region is collected by theobjective lens 50 and reflected by first beam splitter 40 towardsdetector 70.

FIG. 2 illustrates a pupil 60 that is segmented to nine segments—firstpupil segment 61, second pupil segment 62, third pupil segment 63,fourth pupil segment 64, fifth pupil segment 65, sixth pupil segment 66,seventh pupil segment 67, eighth pupil segment 68 and ninth pupilsegment 69.

FIG. 2 illustrates detectors 70 as including nine detectors that arearranged in a 3×3 grid—the nine detectors include first detector 71,second detector 72, third detector 73, fourth detector 74, fifthdetector 75, sixth detector 76, seventh detector 77, eighth detector 78and ninth detector 79—one detector per pupil segment.

The nine image generate nine images that differ from each other—theimages include first image 91, second image 92, third image 93, fourthimage 94, fifth image 95, sixth image 96, seventh image 97, eighth image98 and ninth image 99—one image per detector.

FIG. 3 illustrates a scanning of the region of the object along theyaxis. It is noted that the scanning can occur along any other axis.

FIG. 4 illustrates system 12 that implements an allocation of eighteendetectors to nine pupil segments and to two polarizations—one detectorper combination of pupil segment and polarization.

First detector till ninth detector 71-79 (collectively denoted 70) areallocated for a first polarization and for nine pupil segments.

Eighth detector till eighteenth detector 71′, 72′, 73′, 74′, 75′, 76′,77′, 78′ and 79′ (collectively denoted 70′) are allocated for a secondpolarization and for nine pupil segments.

The different in the polarization aimed to detector 70 and to detectors70′ may be introduced using polarizing beam splitters and/or byinserting polarizing elements before the detectors. It should be notedthat full characterization of polarization may require applying at leastthree different polarizations on each (and not just two)—thus additionalpolarizing elements and additional detectors should be added.

FIG. 4 illustrates a system 13 that includes a first polarizing beamsplitter 81, a second polarizing beam splitter 82 and a third polarizingbeam splitter 83.

FIG. 5 illustrates a system 13 in which different detectors receivereflected radiation at different polarizations—due to the lack of apolarizer before third detector 73, the positioning of a first polarizer89 before first detector 71 and the positioning of second polarized 88before second detector. Each detectors out of first detector 71, seconddetector 72 and third detector 73 receives radiation from the entirepupil.

For simplicity of explanation the radiation source is not shown in FIG.6.

The optics of system 13 includes, first beam splitter 40 and additionalbeam splitters (such as second beam splitter 86 and third beam splitter87) for splitting the beam from the object between the first, second andthird detectors.

In the following text it is assumed that there are nine different pupilsegments, that nine difference images are calculated and that theneighbourhood of each pixel include eight pixels—so that the pixel andhis neighbours include nine pixels. The number of pupil segments maydiffer from nine, the number of differential images may differ fromnine, and the number of pixels neighbours may differ from eight.

Under these assumptions, each region pixel is represented by a vector ofeighty-one elements and a covariance matrix include 81×81 elements. Thenumber of elements per vector may differ from eighty-one, and the numberof elements of the covariance matrix may differ from 81×81. Forexample—the number of pixel neighbours may differ by more than one thenthe number of difference images.

FIG. 6 illustrates first difference image 111, second difference image112, third difference image 113, fourth difference image 114, fifthdifference image 115, sixth difference image 116, seventh differenceimage 117, eighth difference image 118 and ninth difference image 119.

FIG. 6 also illustrates the (m,n)'th pixel of each of the ninedifference images and its eight neighboring pixels —111(m−1,n−1) till111(m+1,n+1), 112(m−1,n−1) till 112(m+1,n+1), 113(m−1,n−1) till113(m+1,n+1), 114(m−1,n−1) till 114(m+1,n+1), 115(m−1,n−1) till115(m+1,n+1), 116(m−1,n−1) till 116(m+1,n+1), 117(m−1,n−1) till117(m+1,n+1), 118(m−1,n−1) till 118(m+1,n+1), and 119(m−1,n−1) till119(m+1,n+1).

The nine difference images represent the same region of the wafer. Eachlocation on the region (also referred to as a region pixel) isassociated with nine pixels of the nine difference images—that arelocated at the same location within the respective difference images.

A (m,n)'th region pixel may be represented by a vector (Vdata(m,n)) thatincludes values (such as intensity) related to the (m,n)'th pixels ofeach of the nine difference images and to values related to theneighboring pixels of the (m,n)'th pixels of each of the nine differenceimages.

For example—for the (m,n)'th region pixel the vector (Vdata(m,n)) mayinclude the following eighty one vector elements—I[111(m−1,n−1)] . . .I[111(m+1,n+1)], I[112(m−1,n−1)] . . . I[112(m+1,n+1)I, I[113(m−1,n−1)]. . . I[113(m+1,n+1)], I[114(m−1,n−1)] . . . I[114(m+1,n+1)],I[115(m−1,n−1)] . . . I[115(m+1,n+1)], I[116(m−1,n−1)] . . .I[116(m+1,n+1)], I[117(m−1,n−1)] . . . I[117(m+1,n+1)], I[118(m−1,n−1)]. . . I[118(m+1,n+1)], and I[119(m−1,n−1)] . . . I[119(m+1,n+1)].

The noise may be estimated by a covariance matrix. The covariance matrixmay be calculated by: (a) for each region pixel calculating all thepossible multiplications between pairs of vector elements—thus foreighty one vector elements of each vector there are 81×81multiplications, (b) summing corresponding products for all of theregion pixels, and (c) normalizing the sums to provide the covariancematrix.

The normalizing may include averaging the sums.

For example—the first eighty one multiplications of step (a) may includemultiplying I[111(m−1,n−1)] by all elements of V(m,n), and the lasteighty one multiplications of step (a) may include multiplyingI[119(m+1,n+1)] by all elements of V(m,n).

Assuming that there are M×N region pixels (and M×N pixels per eachdifference image) than step (a) includes calculating M×N×81×81 products.Step (b) includes performing, generating 81×81 sums—each sum is of M×Nelements, and step (c) include normalizing the 81×81 sums—for example bycalculating an average—dividing each sum by nine.

Assuming that a defect is known or estimated—the defect may berepresented by a defect vector (Vdefect) of eighty one elements.

The decision of whether a region pixel includes a defect may includecalculating the relationship (such as ratio) between the probabilitythat the region pixel (represented by vector Vdata) was obtained by adefect to the probability that the region pixel (represented by vectorVdata) was not obtained by due to a defect.

A decision, per region pixel, of whether the region pixel is defectivemay include comparing a product involving Vdefect^(T), the covariancematrix and Vdata to a threshold TH. If the product exceeds TH then it isdetermined that the region pixel represents a defect else it is assumedthat the region pixel does not include a defect.

Other decision can be made, the threshold may be calculated in anymanner, may be fixed, may change over time, and the like. The samethreshold may be applied for all region pixels—but this is notnecessarily so and different thresholds may be calculated for differentregion pixels. Differences in thresholds may result, for example fromnon-uniformity in the optics, aberrations, and the like different partsin the die may have different properties too and require differentthreshold.

FIG. 7 illustrates first difference image 111, second difference image112, third difference image 113, fourth difference image 114, fifthdifference image 115, sixth difference image 116, seventh differenceimage 117, eighth difference image 118 and ninth difference image 119.

FIG. 7 also illustrates first reference image 101, second referenceimage 102, third reference image 103, fourth reference image 104, fifthreference image 105, sixth reference image 106, seventh reference image107, eighth reference image 108 and ninth reference image 109.

FIG. 7 further illustrates nine images acquired by nine detector—thenine images (also referred to acquired images) include first image 91,second image 92, third image 93, fourth image 94, fifth image 95, sixthimage 96, seventh image 97, eighth image 98 and ninth image 99.

First difference image 111 represents the difference between first image91 and first reference image 101.

Second difference image 112 represents the difference between secondimage 92 and second reference image 102.

Third difference image 113 represents the difference between third image93 and third reference image 103.

Fourth difference image 114 represents the difference between fourthimage 94 and fourth reference image 104.

Fifth difference image 115 represents the difference between fifth image95 and fifth reference image 105.

Sixth difference image 116 represents the difference between sixth image96 and sixth reference image 106.

Seventh difference image 117 represents the difference between seventhimage 97 and seventh reference image 107.

Eighth difference image 118 represents the difference between eighthimage 98 and eighth reference image 108.

Ninth difference image 119 represents the difference between ninth image99 and ninth reference image 109.

FIG. 8 is an example of first difference image 111, second differenceimage 112, third difference image 113, fourth difference image 114,fifth difference image 115, sixth difference image 116, seventhdifference image 117, eighth difference image 118, ninth differenceimage 119, first image 91, second image 92, third image 93, fourth image94, fifth image 95, sixth image 96, seventh image 97, eighth image 98and ninth image 99.

The invention may also be implemented in a computer program for runningon a computer system, at least including code portions for performingsteps of a method according to the invention when run on a programmableapparatus, such as a computer system or enabling a programmableapparatus to perform functions of a device or system according to theinvention.

A computer program is a list of instructions such as a particularapplication program and/or an operating system. The computer program mayfor instance include one or more of: a subroutine, a function, aprocedure, an object method, an object implementation, an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system.

The computer program may be stored internally on a non-transitorycomputer-readable medium. All or some of the computer program may beprovided on computer-readable media permanently, removably or remotelycoupled to an information processing system. The computer-readable mediamay include, for example and without limitation, any number of thefollowing: magnetic storage media including disk and tape storage media;optical storage media such as compact disk media (e.g., CD-ROM, CD-R,etc.) and digital video disk storage media; nonvolatile memory storagemedia including semiconductor-based memory units such as FLASH memory,EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatilestorage media including registers, buffers or caches, main memory, RAM,etc.

A computer process typically includes an executing (running) program orportion of a program, current program values and state information, andthe resources used by the operating system to manage the execution ofthe process. An operating system (OS) is the software that manages thesharing of the resources of a computer and provides programmers with aninterface used to access those resources. An operating system processessystem data and user input, and responds by allocating and managingtasks and internal system resources as a service to users and programsof the system.

The computer system may for instance include at least one processingunit, associated memory and a number of input/output (I/O) devices. Whenexecuting the computer program, the computer system processesinformation according to the computer program and produces resultantoutput information via I/O devices.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

The connections as discussed herein may be any type of connectionsuitable to transfer signals from or to the respective nodes, units ordevices, for example via intermediate devices. Accordingly, unlessimplied or stated otherwise, the connections may for example be directconnections or indirect connections. The connections may be illustratedor described in reference to being a single connection, a plurality ofconnections, unidirectional connections, or bidirectional connections.However, different embodiments may vary the implementation of theconnections. For example, separate unidirectional connections may beused rather than bidirectional connections and vice versa. Also,plurality of connections may be replaced with a single connection thattransfers multiple signals serially or in a time multiplexed manner.Likewise, single connections carrying multiple signals may be separatedout into various different connections carrying subsets of thesesignals. Therefore, many options exist for transferring signals.

Although specific conductivity types or polarity of potentials have beendescribed in the examples, it will be appreciated that conductivitytypes and polarities of potentials may be reversed.

Each signal described herein may be designed as positive or negativelogic. In the case of a negative logic signal, the signal is active lowwhere the logically true state corresponds to a logic level zero. In thecase of a positive logic signal, the signal is active high where thelogically true state corresponds to a logic level one. Note that any ofthe signals described herein can be designed as either negative orpositive logic signals. Therefore, in alternate embodiments, thosesignals described as positive logic signals may be implemented asnegative logic signals, and those signals described as negative logicsignals may be implemented as positive logic signals.

Furthermore, the terms “assert” or “set” and “negate” (or “deassert” or“clear”) are used herein when referring to the rendering of a signal,status bit, or similar apparatus into its logically true or logicallyfalse state, respectively. If the logically true state is a logic levelone, the logically false state is a logic level zero. And if thelogically true state is a logic level zero, the logically false state isa logic level one.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturescan be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented assoft or code representations of physical circuitry or of logicalrepresentations convertible into physical circuitry, such as in ahardware description language of any appropriate type.

Also, the invention is not limited to physical devices or unitsimplemented in non-programmable hardware but can also be applied inprogrammable devices or units able to perform the desired devicefunctions by operating in accordance with suitable program code, such asmainframes, minicomputers, servers, workstations, personal computers,notepads, personal digital assistants, electronic games, automotive andother embedded systems, cell phones and various other wireless devices,commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

What is claimed is:
 1. A method for obtaining information about a regionof a sample, the method comprises: obtaining, by an imager, multipleimages of the region; wherein the multiple images differ from each otherby at least one parameter selected out of illumination spectrum,collection spectrum, illumination polarization, collection polarization,angle of illumination, angle of collection, and sensing type; whereinthe obtaining of the multiple images comprises illuminating the regionand collecting radiation from the region; wherein the region comprisesmultiple region pixels; receiving or generating multiple referenceimages; generating, by an image processor, multiple difference imagesthat represent differences between the multiple images and the multiplereference images; calculating a set of region pixel attributes for eachregion pixel of the multiple region pixels; wherein the calculating isbased on pixels of the multiple difference images; calculating a set ofnoise attributes, based on multiple sets of region pixels attributes ofthe multiple region pixels; and determining for each region pixel,whether the region pixel represents a defect of interest, based on arelationship between the set of noise attributes and the set of regionpixel attributes of the pixel; wherein calculating the set of noiseattributes by calculating a covariance matrix; and, wherein thecalculating of the covariance matrix comprises: calculating, for eachregion pixel, a set of covariance values that represent the covariancebetween different attributes of the set of region pixel attributes ofthe region pixel; and calculating the given covariance matrix based onmultiple sets of covariance values of the multiple region pixels.
 2. Themethod according to claim 1 wherein determining whether the region pixelrepresents a defect of interest is also responsive to a set ofattributes of an actual defect.
 3. The method according to claim 1wherein determining whether the region pixel represents a defect ofinterest is also responsive to a set of attributes of an estimateddefect.
 4. The method according to claim 1, wherein the set of pixelattributes of a region pixel comprises data regarding the region pixeland neighboring region pixels of the region pixel.
 5. The methodaccording to claim 1, wherein the imager comprises multiple detectorsfor generating the multiple images, and wherein the method comprisesallocating different detectors to detect radiation from different pupilsegments of multiple pupil segments.
 6. The method according to claim 5,wherein a number of the different pupil segments of the multiple pupilsegments exceeds four pupil segments.
 7. The method according to claim1, wherein the imager comprises multiple detectors for generating themultiple images, and wherein the method comprises allocating differentdetectors to detect radiation from different combinations of (a)polarization and (b) different pupil segments of multiple pupilsegments.
 8. The method according to claim 1, further comprisingobtaining the multiple images at a same point in time.
 9. The methodaccording to claim 1, further comprising obtaining the multiple imagesat different points in time.
 10. The method according to claim 1,further comprising classifying the defect of interest.
 11. Acomputerized system for obtaining information about a region of asample, the system comprises: an imager that comprises optical elementsand an image processor; wherein the imager is configured to obtainmultiple images of the region; wherein the multiple images differ fromeach other by at least one parameter selected out of illuminationspectrum, collection spectrum, illumination polarization, collectionpolarization, angle of illumination, and angle of collection; whereinthe obtaining of the multiple images comprises illuminating the regionand collecting radiation from the region; wherein the region comprisesmultiple region pixels; wherein the computerized system is configured toreceive or generate multiple reference images; wherein the imageprocessor is configured to: generate multiple difference images thatrepresent differences between the multiple images and the multiplereference images; calculate a set of region pixel attributes for eachregion pixel of the multiple region pixels; wherein the set of regionpixel attributes are calculated based on pixels of the multipledifference images; calculate a set of noise attributes, based onmultiple sets of region pixels attributes of the multiple region pixels;and determine, for each region pixel, whether the region pixelrepresents a defect of interest, based on a relationship between the setof noise attributes and the set of region pixel attributes of the pixel,wherein calculating the set of noise attributes by calculating acovariance matrix; and, wherein the calculating of the covariance matrixcomprises: calculating, for each region pixel, a set of covariancevalues that represent the covariance between different attributes of theset of region pixel attributes of the region pixel; and calculating thegiven covariance matrix based on multiple sets of covariance values ofthe multiple region pixels.
 12. A non-transitory computer-readablemedium that stores instructions that cause a computerized system to:obtain, by an imager of the computerized system, multiple images of aregion of an object; wherein the multiple images differ from each otherby at least one parameter selected out of illumination spectrum,collection spectrum, illumination polarization, collection polarization,angle of illumination, angle of collection, and sensing type; whereinthe obtaining of the multiple images comprises illuminating the regionand collecting radiation from the region; wherein the region comprisesmultiple region pixels; receive or generate multiple reference images;generate, by an image processor of the computerized system, multipledifference images that represent differences between the multiple imagesand the multiple reference images; calculate a set of region pixelattributes for each region pixel of the multiple region pixels; whereinthe calculating is based on pixels of the multiple difference images;calculate a set of noise attributes, based on multiple sets of regionpixels attributes of the multiple region pixels; and determine, for eachregion pixel, whether the region pixel represents a defect of interest,based on a relationship between the set of noise attributes and the setof region pixel attributes of the pixel, wherein calculating the set ofnoise attributes by calculating a covariance matrix; and, wherein thecalculating of the covariance matrix comprises: calculating, for eachregion pixel, a set of covariance values that represent the covariancebetween different attributes of the set of region pixel attributes ofthe region pixel; and calculating the given covariance matrix based onmultiple sets of covariance values of the multiple region pixels. 13.The system according to claim 11, wherein determining whether the regionpixel represents a defect of interest is also responsive to a set ofattributes of an actual defect.
 14. The system according to claim 11,wherein determining whether the region pixel represents a defect ofinterest is also responsive to a set of attributes of an estimateddefect.
 15. The system according to claim 11, wherein the set of pixelattributes of a region pixel comprises data regarding the region pixeland neighboring region pixels of the region pixel.
 16. Thenon-transitory computer-readable medium according to claim 12, whereindetermining whether the region pixel represents a defect of interest isalso responsive to a set of attributes of an actual defect.
 17. Thenon-transitory computer-readable medium according to claim 12, whereindetermining whether the region pixel represents a defect of interest isalso responsive to a set of attributes of an estimated defect.
 18. Thenon-transitory computer-readable medium according to claim 12, whereinthe set of pixel attributes of a region pixel comprises data regardingthe region pixel and neighboring region pixels of the region pixel.